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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from playwright.sync_api import sync_playwright
from flax import linen as nn
from jax import random
import jax
import jax.numpy as jnp
# Define LLaVA model parameters
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
MAX_LENGTH = 512
NUM_BEAMS = 5
# Define Flax model for action generation
class ActionModel(nn.Module):
vocab_size: int
hidden_size: int
num_layers: int
def setup(self):
self.embedding = nn.Embed(self.vocab_size, self.hidden_size)
self.lstm = nn.LSTM(self.hidden_size, self.hidden_size, num_layers=self.num_layers)
self.dense = nn.Dense(self.vocab_size)
def __call__(self, inputs, init_state):
embedded = self.embedding(inputs)
output, new_state = self.lstm(embedded, init_state)
logits = self.dense(output)
return logits, new_state
# Initialize Flax model and get its initial state
vocab_size = 50257 # Adjust this if needed for Zephyr-7b-beta
hidden_size = 1024
num_layers = 2
key = random.PRNGKey(0)
model = ActionModel(vocab_size, hidden_size, num_layers)
init_state = model.lstm.initialize_carry(key, (1, hidden_size))
# Function to generate actions using Zephyr-7b-beta model
def generate_actions(input_text, browser, page):
# Load Zephyr-7b-beta model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
# Prepare input for Zephyr-7b-beta
inputs = tokenizer(input_text, return_tensors="pt")
inputs = inputs.to(model.device)
# Generate response (use pipeline for Zephyr-7b-beta)
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
outputs = generator(input_text, max_length=MAX_LENGTH, num_beams=NUM_BEAMS, temperature=0.7)
# Decode response and extract actions
response = outputs[0]['generated_text']
actions = response.split("\n")
# Perform actions
for action in actions:
if "open website" in action:
website = action.split(" ")[-1]
page.goto(website)
elif "click" in action:
selector = action.split(" ")[-1]
page.click(selector)
elif "type" in action:
text = action.split(" ")[-1]
page.type(text)
elif "submit" in action:
page.press("Enter")
else:
print(f"Action not recognized: {action}")
# Function to initialize browser and page
def initialize_browser():
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
return browser, page
# Gradio interface
def run_agent(input_text):
with sync_playwright() as p:
browser, page = initialize_browser()
generate_actions(input_text, browser, page)
return f"Successfully executed actions based on: {input_text}"
iface = gr.Interface(
fn=run_agent,
inputs=gr.Textbox(label="Enter your request"),
outputs=gr.Textbox(label="Response"),
title="Automated Agent",
description="Enter a task or instruction for the agent to perform."
)
iface.launch() |